The histone demethylase PHF8 regulates TGFβ signaling and promotes melanoma metastasis

The contribution of epigenetic dysregulation to metastasis remains understudied. Through a meta-analysis of gene expression datasets followed by a mini-screen, we identified Plant Homeodomain Finger protein 8 (PHF8), a histone demethylase of the Jumonji C protein family, as a previously unidentified prometastatic gene in melanoma. Loss- and gain-of-function approaches demonstrate that PHF8 promotes cell invasion without affecting proliferation in vitro and increases dissemination but not subcutaneous tumor growth in vivo, thus supporting its specific contribution to the acquisition of metastatic potential. PHF8 requires its histone demethylase activity to enhance melanoma cell invasion. Transcriptomic and epigenomic analyses revealed that PHF8 orchestrates a molecular program that directly controls the TGFβ signaling pathway and, as a consequence, melanoma invasion and metastasis. Our findings bring a mechanistic understanding of epigenetic regulation of metastatic fitness in cancer, which may pave the way for improved therapeutic interventions.


INTRODUCTION
Although metastasis accounts for more than 80% of deaths linked to solid malignancies (1), it is an inefficient process, as several steps hinder its progression. Tumor cells need to detach from their primary site, invade the nearby tissue, intravasate, endure circulatory stress, extravasate, and co-opt the host microenvironment to eventually establish a secondary growth (2). Throughout this process, cancer cells must select for genetic and epigenetic traits that are advantageous for their dissemination and adaptation to distal host organs.
The Cancer Genome Atlas (TCGA) has generated extensive genomic data across 33 cancer types, resulting in the identification of 299 driver genes for various cancer types (3). Particularly in melanoma, TCGA and other large sequencing efforts in the past decade have revealed genetically disrupted signaling pathways that contribute to pathogenesis, including gain-of-function mutations or amplification of proto-oncogenes (i.e., BRAF, NRAS, KIT, MITF, CDK4, and MDM2) and loss-of-function mutations or deletion of tumor suppressor genes (i.e., PTEN, TP53, CDNK2A, and RB1) (4). While these genetic alterations play a major role in melanoma initiation or maintenance, none fully explain metastatic behavior or the starkly different outcomes of patients initially diagnosed with primary melanoma. Recent literature supports the importance of epigenetic changes acquired by melanoma cells that lead to changes in their transcriptional output, which, in turn, increase their fitness and metastatic potential (5,6). Because malignant cells with the same genetic features can elicit diverse epigenetic programs to adapt to different microenvironments, we posit that epigenetic regulators might be critical drivers of metastasis.
Here, we mined publicly available gene expression datasets of human melanoma samples (7)(8)(9)(10) to select a stringent subset of epigenetic regulators consistently up-regulated in metastasis as compared to primary tumors. These genes could be markers or mediators of melanoma metastasis and potentially "druggable" targets. Of the list of candidates, we focused on PHF8, a Jumonji C (JmjC) domaincontaining protein that erases repressive histone marks including H4 Lysine 20 monomethyl (H4K20me1) and H3 Lysine 9 monomethyl (H3K9me1) (11). PHF8 is a histone demethylase that preferentially localizes at promoters and participates in cell cycle regulation by removing H4K20me1 from the promoters of E2F Transcription Factor 1 (E2F1)-regulated genes (12). We show that PHF8 is up-regulated in metastatic samples compared to primary melanomas and nevi from an independent patient cohort and regulates invasive and metastatic potential through a mechanism dependent on its histone demethylase function. Moreover, we demonstrate that PHF8 directly controls the transcription of invasion and metastasis-related signatures, particularly the Transforming Growth Factor- (TGF) pathway, which is required for the proinvasive role of PHF8. polycomb group finger ring 2 (PCGF2), chromodomain helicase DNA binding protein 3 (CHD3), and PHF8 (Fig. 1B and table S2). Direct roles for these chromatin-related genes in transformation or melanoma progression have not been reported. Knockdown of five of the six selected genes using two different short hairpin RNAs (shRNAs) significantly impaired proliferation of SKMEL-147 cells (Fig. 1C), with the exception of shCBX8 R2 that did not affect proliferation but proved to be inefficient for CBX8 silencing ( fig. S1C).
Notably, CBX2 knockdown does not only impair proliferation but also leads to apoptosis as shown by caspase-3 cleavage ( fig. S1A). Invasion assays revealed decreased invasive potential upon silencing of all genes, with the exception of PCGF2 (Fig. 1D). In contrast to the other five candidates, PHF8 knockdown, which silenced two different transcript variants (Fig. 1G), did not affect proliferation (Fig. 1E) yet significantly reduced invasion in vitro (Fig. 1F). To decipher mechanisms directly affecting metastasis and patient outcomes, we selected a candidate hit that specifically altered melanoma invasive potential without affecting cell proliferation. Therefore, while all tested candidates merit further characterization of their role in melanoma maintenance and progression, we elected to further examine the role of PHF8 in melanoma invasion and metastasis.

PHF8 consistently regulates melanoma invasion
With the aim of ruling out off-target effects, we used orthogonal methods to knock out PHF8 and assess how general the proinvasive effect of PHF8 is across multiple melanoma cell lines. The loss of invasive capacity observed upon PHF8 knockdown in SKMEL-147 melanoma cells was consistently reproduced using the CRISPR-Cas9 system and two efficient small guide RNAs (sgRNAs) targeting PHF8 (sgPHF8 #1 and sgPHF8 #3) in four melanoma cell lines (SKMEL-147, 501Mel, A375, and 451Lu) (Fig. 2, A to D). PHF8 knockout did not affect proliferation ( Fig. 2B) but significantly inhibited invasion (Fig. 2, C and D). The four cell lines used differ in NRAS and BRAF mutational status, suggesting that PHF8 proinvasive phenotype is not restricted to a particular genetic background. To further confirm the specificity of the observed effects, SKMEL-147 cells stably transduced with PHF8 sgRNAs or scrambled sgRNA (sgScr) were infected with FLAG-hemagglutinin (HA)-tagged PHF8 overexpressing lentiviruses. PHF8 ectopic expression, confirmed by FLAG and PHF8 immunoblots (Fig. 2E), was able to rescue the defect in invasion caused by PHF8 knockout (Fig. 2F). PHF8 loss led to increased H4K20me1 deposition relative to control, sgScrtransduced SKMEL-147 cells (Fig. 2G), as previously demonstrated by Liu et al. (12).

PHF8 is up-regulated in metastatic melanoma patient samples
We analyzed PHF8 protein expression in a panel of human cultured melanocytes (n = 4), primary (n = 7) and metastatic (n = 11) melanoma cell lines. PHF8 expression was low or barely detectable in melanocytes derived from perinatal and adult skin. Primary cell lines displayed variable levels, whereas most metastatic melanoma cell lines expressed high PHF8 levels (Fig. 3A). Similar differences were observed at transcript levels by PHF8 quantitative real-time polymerase chain reaction (qRT-PCR) ( fig. S2). PHF8 mRNA was found significantly up-regulated in metastatic relative to primary patient samples from TCGA data (Fig. 3B) (13), as well as in two of the Affymetrix datasets used in our meta-analysis ( PHF8 overexpression in metastatic melanoma, we performed PHF8 immunohistochemistry on an independent cohort of primary (n = 67) and metastatic (n = 46) melanoma patient samples obtained from the New York University (NYU) Langone Health Melanoma Program. Consistent with our findings in TCGA and in melanoma cell lines, we observed that while primary samples were similarly split between no/low and high PHF8 expression, most metastatic samples expressed high PHF8 levels, both in percentage of positive cells and staining intensity (Fig. 3C). We therefore establish that elevated PHF8 transcriptional levels in metastatic samples relative to primary samples-observed in public gene expression datasetsare consistent with higher PHF8 protein levels. Representative images show different nuclear PHF8 staining intensities in primary and metastatic samples (Fig. 3D). In addition, in a subset of 22 patient-matched melanoma samples, we found a statistically significant increase in PHF8 expression from primary to metastasis (Fig. 3E). These findings link PHF8 expression to melanoma metastasis and disease progression.

PHF8 promotes metastasis in vivo
To investigate the effect of PHF8 knockout on the metastatic capacity of melanoma cells in vivo, 451Lu cells transduced with a luciferaseexpressing construct, Cas9 and sgPHF8 #1, sgPHF8 #3, or sgScr were injected subcutaneously in the flanks of immune-compromised mice. Once palpable, tumor growth was regularly measured by caliper (Fig. 4A). Although PHF8 knockout did not affect primary tumor growth (Fig. 4B) or tumor mass at termination of the experiment (40 days) (Fig. 4C), it led to a significant decrease in lung metastasis burden measured by ex vivo bioluminescence (Fig. 4D), which corresponded to a reduced number of metastatic foci (Fig. 4, E to G). We conclude that PHF8 enhances melanoma metastatic progression without affecting primary tumor growth.
PHF8 regulates melanoma invasive potential through its histone demethylase function Elevated PHF8 mRNA in metastatic samples is consistent with higher nuclear protein expression by immunohistochemistry (Fig. 3, C to E), suggesting that its role as histone demethylase and transcriptional activator could contribute to its proinvasive effect. We addressed whether PHF8 demethylase activity is required for its role in melanoma invasion. We used two different mutant constructs that impair the PHF8 demethylase activity in different ways (11). PHF8 F279S harbors a single point mutation in the JmjC domain that impairs its catalytic activity, while the PHF8 Y14A/W29A construct contains two mutations in the PHD domain (Fig. 5A). PHF8 binds to H3K4me3, a histone mark of active promoters, via its Plant Homeodomain (PHD) domain, allowing the linker region between the PHD and JmjC domain to adopt a bent conformation and interact with and demethylate H3K9me1/me2 (14). Therefore, the latter mutant construct also impairs the demethyl ase activity by preventing PHF8 recruitment to transcription start sites (TSSs). The overexpression of wild-type (WT) PHF8, but not of its mutant forms, decreases H4K20me1 levels ( Fig. 5A, right). We stably transduced 451Lu, Colo-679, and 113/6-4L (15) melanoma cell lines with Empty, PHF8 WT, PHF8 F279S, or PHF8 Y14A/W29A-carrying lentiviral particles (Fig. 5B). While none of the transduced PHF8 WT or mutant constructs significantly affected proliferation (Fig. 5C), we observed that PHF8 WT overexpression significantly increases invasion in all three cell lines (Fig. 5D). These data support that PHF8 overexpression is sufficient to increase melanoma cell invasion. However, both mutant forms

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failed to promote invasion, demonstrating that the demethylase activity of PHF8 is required for its role in invasion (Fig. 5D). Accordingly, only PHF8 WT, and not the mutants lacking demethylase activity, was able to rescue the defect in invasion observed in PHF8 knockout melanoma cells (Fig. 5, E and F).

PHF8 controls the transcription of invasion and metastasis-related signatures
To investigate how PHF8 contributes to the metastatic process, we further delved into the transcriptional changes triggered by PHF8 modulation in melanoma cells. Chromatin immunoprecipitation sequencing (ChIP-seq) analyses reveal that PHF8 binds mostly active promoters (Fig. 6, A and B), which concurs with previous studies describing its function as a transcriptional activator (12). However, although we were able to recapitulate the antiproliferative effects and E2F1 down-regulation previously observed in HeLa cells upon PHF8 depletion ( fig. S3, A to F) (12), we found that, unlike TGFB1 promoter, the E2F1 promoter was not significantly bound by PHF8 in SKMEL-147 cells ( fig. S3G). In addition, we consistently show using multiple cell lines that E2F1 is not a transcriptional target of PHF8 in melanoma ( fig. S3, H and I). To better understand the molecular mechanisms underlying PHF8 regulation of invasion and metastasis, we performed RNA sequencing (RNA-seq) to compare the transcriptome of sgPHF8 (using two different sgRNAs) versus sgScr-transduced cell lines. A significant number of genes (4304 genes) are differentially regulated in sgPHF8 relative to sgScr-transduced cells [P < 0.05; false discovery rate (FDR) ≤ 0.15] (Fig. 6C). Ingenuity Pathway Analysis was performed on a smaller list of 2573 genes (P < 0.01; FDR < 0.05) to which an additional cutoff of changes in expression was applied (−0.3 < log 2 fold change < +0.3). This analysis revealed that PHF8 controls the transcription of multiple invasion and metastasis-related genes, such as integrins, matrix metalloproteinases (MMPs), and A Disintegrin and Metalloproteinase (ADAM) proteins, as well as TGF signaling, the most significantly modulated pathway at the transcriptional level (Fig. 6D). Several ligands, receptors, and transcription factors involved in the TGF signaling pathway are positively regulated by PHF8 (Fig. 6E). The overlap of PHF8 targets identified by ChIP-seq (6118 significant peaks) with the 4304 genes transcriptionally modulated by PHF8 deletion revealed 1564 genes, the expression of which is directly regulated by PHF8 binding. This category comprises MMPs, integrins, and ADAM proteins, as well as genes of the canonical TGF signaling pathway (Fig. 6F), which are directly bound by PHF8 at their TSS (Fig. 6G).
As another readout of PHF8 loss-of-function, we examined the deposition of its major substrate and repressive histone marks, H3K9me1 and H4K20me1, at promoter regions of PHF8 direct targets, TGFB1, TGFBR1, and TGFBR2. ChIP-qPCR data indicate that those histone marks are enriched in sgPHF8-transduced cells as compared to their scrambled control at the TSS regions of TGFB1, TGFBR1, and TGFBR2 that are occupied by PHF8 (Fig 6, G and H). Moreover, overexpression of PHF8 WT, but not of its mutant forms, suppresses H4K20me1 and H3K9me1 deposition at the TSS regions of TGFB1 and TGFBR1 ( fig. S4). Together, our data demonstrate that PHF8 is a direct transcriptional activator of prometastatic genes, notably several genes of the TGF pathway, via demethylation of its substrates, the repressive histone marks H3K9me1 and H4K20me1.         . S5B). As shown in Fig. 6E, PHF8 knockout down-regulates TGFB1, TGFBR1, and TGFBI, a downstream signaling target of the TGF signaling cascade (fig. S5C). Therefore, we postulated that PHF8 positively regulates TGF signaling. The canonical TGF signaling cascade is initiated when TGF ligands (i.e., TGFB1) bind to the type II transmembrane receptor serine/threonine kinase TGFBR2, which, in turn, assembles with, phosphorylates, and activates the type I receptor TGFBR1. Activated TGFBR1 phosphorylates the downstream effectors SMAD2 and SMAD3, which then associate with SMAD4. The formed complex accumulates in the nucleus where it regulates the transcription of various target genes (16). We found that basal levels of P-SMAD2 (Ser 465/467 ), a marker of TGF signaling, are notably down-regulated in sgPHF8-transduced SKMEL-147 and 113/6-4L cells as compared to their sgScr control (Fig. 7A). Consistent with these findings, PHF8 loss-of-function reduced SMAD transcriptional activity, as measured by a luciferase reporter assay (Fig. 7B, top). Changes in SMAD activity in the presence of TGF or galunisertib support the specificity of the reporter assay (Fig. 7B,  bottom). Conversely, PHF8 overexpression in Colo-679 and 113/6-4L cells resulted in increased SMAD2 phosphorylation relative to empty vector-transduced control cells (Fig. 7C). PHF8 induces P-SMAD2 in a histone demethylase-dependent manner, because PHF8 mutants are unable to up-regulate P-SMAD2 (Fig. 7C). Furthermore, we established that TGF signaling is required for PHF8 proinvasive phenotype, because the increase in invasion observed upon PHF8 overexpression in Colo-679 and 113/6-4L cells (Fig. 7D) can be significantly reversed by pre-treatment with TGF receptor inhibitors galunisertib and SB431542 (Fig. 7D) or genetic deletion of TGFBR2 (Fig. 7F). We further confirm that PHF8 is an upstream regulator of TGF signaling, because efficient pharmacological (Fig. 7G) or genetic inhibition of this pathway does not reduce PHF8 expression (Fig. 7H). Overall, these data demonstrate that PHF8 directly orchestrates a transcriptional proinvasive program that comprises several components of the TGF pathway, activates TGF signaling, and promotes melanoma invasion and metastasis (Fig. 8).
In agreement with our proposed model, PHF8 levels positively correlate with the expression of its transcriptional targets (e.g., SMAD3 and SMAD4; Fig. 6E

DISCUSSION
Melanomas are highly metastatic tumors, yet despite intensive genome sequencing efforts, no genetic alterations that explain metastatic progression and melanoma patient outcomes have been identified.
Here, we investigated the ability of epigenetic regulators, which orchestrate changes in transcriptional programs, to confer metastatic fitness to malignant cells. We performed an unbiased meta-analysis of publicly available gene expression datasets to identify candidate epigenetic regulators altered in metastatic versus primary melanoma. We expected this approach would reduce false positives in candidate selection by limiting biases due to differences in sample processing, technical considerations, and cohort size and increase our chances of discovering genes truly involved in melanoma progression. All six chromatin-related genes tested in our loss-of-function mini-screen proved to be essential for melanoma proliferation and/ or invasion, thus supporting the value of our strategy. CBX2, CBX4, and CBX8 are canonical components of the Polycomb repressive complex 1 (PRC1), responsible for its targeting to chromatin through physical interaction with H3K9me3 and H3K27me3 marks via their chromodomains (17,18). PCGF2 is also a component of the PRC1 core complex and maintains the transcriptional repression of genes involved in embryogenesis, cell cycle, and tumor suppression (19). Polycomb group proteins have been involved in a variety of biological processes, such as X-chromosome inactivation, maintenance of pluripotency, self-renewal capacity in embryonic stem cells, cell fate decisions, and developmental processes (20). Moreover, Polycomb genes are frequently found mutated or deregulated in cancer (21,22). CHD3 is a chromatin remodeler containing a SNF2-related helicase/ adenosine triphosphatase domain and a component of the histone deacetylase complex referred to as the Mi-2/NuRD complex, which participates in the remodeling of chromatin through histone deacetylation (23). Each of these genes represents attractive candidates as therapeutic targets for advanced melanoma and awaits further investigation in follow-up studies. To better understand mechanisms of melanoma metastasis, we opted to focus on PHF8, the modulation of which affected invasion without altering cell proliferation. Melanoma cells are able to switch via transcriptional reprogramming between cellular states, categorized as proliferative or invasive, both of which are marked by distinct gene expression signatures (24,25). Because individual cells have been shown to switch back and forth between those states, genetic mutations cannot underlie these cellular phenotypes (26,27). However, epigenetic factors might trigger changes in transcriptional output that ultimately increase melanoma cells aggressiveness. Hence, isolated melanoma cells expressing the H3K4 demethylase JARID1B characterize a slow-cycling tumorigenic cell population (28) that is associated with resistance to therapy (29).
Here, we identified PHF8 as a prometastatic factor. PHF8 is a histone demethylase that binds through its PHD domain to H3K4me3, an active histone mark located at TSSs, and is thus recruited and enriched at target gene promoters (30). Notably, PHF8 plays a role in various developmental and disease processes. Mutations in PHF8 are associated with X-linked mental retardation and cleft lip/cleft palate (31), and the modulation of histone methylation by PHF8 plays a critical role in neuronal differentiation and brain and craniofacial development (11). PHF8 overexpression in malignant cells has been reported in several cancers, including acute lymphoblastic leukemia (32), breast cancer (33), colorectal cancer (34), gastric cancer (35,36), prostate cancer (37)(38)(39)(40), and hepatocellular carcinoma (41). However, PHF8 occupancy and its downstream transcriptional programs in those tumor types have not been elucidated. We report that, unlike other cell types such as HeLa cells ( fig. S3), PHF8 does not regulate cell cycle progression in melanoma. It is critical to gain a better understanding of the downstream mechanisms of PHF8 in various contexts, and whether modulation of the TGF pathway is a more general finding.
The modulation of TGF signaling and invasion by PHF8 offers the possibility of targeting PHF8 to inhibit this pathway. To date, while inhibitors of the JmjC demethylases have been reported, few have shown sufficient potency and selectivity toward subfamily members   sharing high sequence identity, such as PHF8, PHF2, and Lysine Demethylase 7A (KDM7A or KIAA1718) (42). The development of novel pyridine derivatives in which the introduction of specific substituents is used to modulate the selectivity profile against histone lysine demethylases is ongoing (43), but their efficacy in cellular assays has not yet been reported. We found that PHF8 epigenetically regulates TGF signaling through transcriptional activation of ligands and receptors of the pathway. Shao et al. (44) reported that MYC posttranscriptionally regulates PHF8 in breast cancer. In this context, MYC and TGF treatment cooperate to up-regulate PHF8, thus contributing to proliferation and epithelial-mesenchymal transition (EMT) through transcriptional up-regulation of SNAI1 and ZEB1. They did not report TGFB1 and its receptors as direct downstream targets of PHF8, as we find in melanoma. In further contrast, our transcriptomic analyses did not reveal a modulation of EMT genes, including ZEB1, but rather pointed to a TGF pathway signature, encompassing ligands and receptors, as direct PHF8 downstream effectors. These findings highlight the pleiotropic roles of epigenetic regulators across different cellular and tumor types. Among the genes directly modulated by PHF8, we found TGF-induced (TGFBI) (Fig. 6, E and G), a secreted extracellular matrix component that confers high metastatic potential to melanoma cells (45). Cell autonomous activation of the TGF pathway in melanoma cell lines has been well documented (46). TGF signaling through TGFBR2 expression enhances melanoma invasion and motility, and TGF ligands induce their own expression through a positive amplification loop (47). Clinically, one study reported that TGF plasma levels are elevated in patients with melanoma and correlate with metastatic progression (48).
Unfortunately, harnessing the TGF pathway as a therapeutic target in cancer has long been hindered by the pleiotropic nature of its signaling effects. In early-stage tumors, TGF pathway activation leads to cytostatic and apoptotic tumor-suppressive responses. However, tumor cells hijack the tumor-suppressive responses to TGFB1 and convert this signal into an oncogenic factor (49). Intriguingly, genome-wide expression analysis of nearly 100 human melanoma cell lines demonstrated that the coordinated expression of a number of genes reminiscent of a TGF signature associates with a highly invasive phenotype and low proliferation rate (27). Therefore, the inhibition of the TGF signaling cascade could be effective in preventing dissemination. Several TGF-targeting agents, used as single agents or in combination, have been tested in clinical trials with some promising results in different cancers (50). TGF signaling can cross-talk with the mitogen-activated protein kinase (MAPK) pathway and contribute to resistance to B-Raf proto-oncogene (BRAF) and MAPK kinase (MEK) inhibitors (51). Accordingly, PHF8 modulation or TGF inhibition may limit the development of treatment resistance when combined with MAPK inhibitors.
Aside from a potential impact on response to targeted therapies, the epigenetic regulation of the TGF pathway by PHF8 could significantly improve response to checkpoint inhibitors. It has long been established that TGF secretion from tumor cells represses the production of cytolytic and proapoptotic factors by CD8 + cytotoxic T lymphocytes (52). Moreover, recent studies elegantly demonstrated that high TGF signaling in metastatic tumor margins contributes to reduced immune surveillance and poor therapy response in metastatic colorectal (53) and urothelial (54) cancers. The precise role of the PHF8-TGF signaling axis in immune infiltration of melanoma tumors and response to immunotherapy merits further examination. Our data suggest that interfering with PHF8 expression might improve response to checkpoint blockade by inhibiting TGF signaling, yielding better clinical outcomes in melanoma patients with tumors refractory to programmed cell death protein 1 (PD1) inhibition.
In summary, we report a mechanism of epigenetic regulation of the TGF pathway by PHF8 in melanoma cells that specifically governs melanoma metastasis. Our findings reveal new avenues for therapeutic intervention to improve patient outcomes.

Data mining of human transcriptomics datasets
The log fold changes in gene expression between metastases and primaries or nevi were calculated in the following four Affymetrix

Viral transduction
Target cells were seeded, incubated overnight before infection, and transduced at 30% of cell confluence. Medium was replaced with 1:4 diluted viral supernatant and Polybrene (4 g/ml; EMD Millipore) and incubated for 6 hours, followed by replacement with growth medium. Cells were checked for fluorescent protein expression or added drug selection agents on subsequent days to ensure pure populations of transduced cells.

Quantitative real-time PCR
RNA was extracted using the RNeasy mini kit (Qiagen) and following the manufacturer's recommendations. Eluted RNA was quantified by Nanodrop 2000 or Qubit (Thermo Fisher Scientific) following the manufacturer's recommendations and stored at −80C. One microgram of RNA was reverse-transcribed using the MultiScribe Reverse Transcriptase Kit (Applied Biosystems) with random hexamers (Invitrogen) following the manufacturer's recommendations.
Transcripts were quantified by qRT-PCR using Power SYBR Green qPCR MasterMix (Invitrogen). Cycle threshold values were normalized to those of the housekeeping gene GAPDH (glyceraldehyde phosphate dehydrogenase). The average for three biological replicates was plotted as relative transcript abundance. All reactions were performed in triplicate using Biorad CFX 384 or CFX 96 real-time cyclers. All primer sequences are listed in table S2.

Plasmid preparation
All plasmid constructs were propagated in Stbl3 (Thermo Fisher Scientific) or XL-1 Blue Ultracompetent bacteria (Agilent Technologies) on LB plates or in LB media with appropriate antibiotics. Plasmids were extracted by mini-or maxi-prep (Qiagen) following the manufacturer's recommendations. All cloned constructs were verified by Sanger sequencing before use. Overexpression constructs Retroviral constructs expressing PHF8 WT or the mutant forms PHF8 F279S and PHF8 Y14A/W29A were subcloned into lentiviral constructs with puromycin resistance. sgRNA cloning An optimal gRNA target sequence closest to the genomic target site was chosen using the http://crispr.mit.edu/ design tool. Four sgRNAs per gene that predict the best scores and lowest number of potential off-targets in exonic regions were chosen. All sgRNA sequences validated and used are listed in the table S2. The sgRNA oligonucleotides (Integrated DNA Technologies) were resuspended in annealing buffer [10 mM tris (pH 7.5 to 8.0), 50 mM NaCl, and 1 mM EDTA], mixed in equimolar concentrations, and annealed by incubation at 95°C for 5 min, followed by a slow cooling to room temperature. Annealed oligonucleotides were cloned using Bbs I (New England Biolabs) sites downstream of the human U6 promoter in a lentiviral vector containing enhanced green fluorescent protein downstream of the human PGK promoter (pLKO-sgRNA-GFP; a gift of the Brown laboratory, Mount Sinai School of Medicine, NY). Lentiviral vectors were produced as above. Melanoma cell lines stably expressing Cas9 were generated by infection with the lentiCas9-Blast (Addgene, catalog no. 52962) or Lenti-dCas9-KRAB-Blast (Addgene, catalog no. 89567) lentiviral plasmid, followed by selection with blasticidin (10 g/ml). Cells were then infected with pLKO-sgRNA-GFP. Cells were transduced at more than 95% efficiency, and efficient knockout was assessed by Western blot 4 to 5 days after transduction. shRNA constructs plkO.1 plasmids carrying shRNA targeting human CBX2/4/8, PCGF2, CHD3, PHF8 (Sigma-Aldrich), and a nontargeting control (Dharmacon) were purchased (table S3). Lentiviral vectors and transduction were performed as detailed above.
SMAD luciferase reporter assay SKMEL-147 cells transduced with sgScr or sgRNAs targeting PHF8 were infected with SMAD binding element (SBE) luciferase reporter lentiviral particles (BPS Biosciences) in white opaque 96-well plates. Two days later, the luminescence assay was performed using the One-Step Luciferase Assay System (BPS Biosciences) according to the manufacturer's instructions. In an independent set of experiments, 24 hours after SBE reporter transduction, cells were serum-deprived overnight, followed by treatment with TGF (10 ng/ml) or galunisertib (10 M) for 12 hours, before measurement of SMAD activity by luciferase assay.

In vitro invasion assay
Cell invasion was measured using 24-well Fluoroblok transwell inserts (Becton Dickinson, 8 m pore). Briefly, inserts were coated for 2 hours at 37°C with Matrigel (Becton Dickinson/Corning) diluted in coating buffer [0.01 M tris-HCl (pH 8) and 0.7% NaCl]. For invasion experiments using cells treated with TGF or TGF inhibitor, inserts were coated with fibronectin (10 g/ml; Becton Dickinson) to circumvent the effect of potential traces of TGF in Matrigel. Cells were harvested, counted in triplicate, washed, and resuspended in serum-free growth medium. Melanoma cells (20,000 to 30,000 cells per condition) were seeded per coated Fluoroblok inserts and corresponding control wells in cell input plate. Cells were allowed to settle for 10 min, followed by addition of complete growth media to the lower chamber, as chemoattractant. Twelve to 16 hours after seeding, invading cells were post-stained with Calcein AM (Thermo Fisher Scientific) diluted to 1 g/ml in prewarmed 1× Hanks' balanced salt solution (HBSS) for 30 min. For each independent experiment, three inserts per condition were used. Six random fields per insert were imaged using a 10× objective on an inverted fluorescence microscope. Invading cells were counted using the ImageJ software. The average of cell counts from four inserts per condition was used for plotting results.
Cell input control wells were fixed for 15 min with 1% glutaraldehyde diluted in 1× phosphate-buffered saline (PBS), washed twice with 1× PBS, and stained with a 0.5% crystal violet solution for 30 min to 2 hours at room temperature, followed by extensive washing with diH 2 O. Cells were destained with 15% acetic acid and quantified by absorbance at 595 nm. Counts of invading cell for each well were normalized to the mean absorbance of the corresponding condition from the cell input plate to control for variations in cell seeding. Only experiments with minimal variations in cell seeding between different conditions were considered.

Western blots
Protein lysates were generated using radioimmunoprecipitation assay (RIPA) buffer (Thermo Fisher Scientific) supplemented with protease inhibitors (cOmplete EDTA-free, Roche) and phosphatase inhibitors (PhosStop, Roche) for 20 min on ice, followed by centrifugation for 15 min at 13,000 rpm at 4°C. The protein-containing supernatant was transferred to fresh microcentrifuge tubes and stored at −20° or − 80°C until further use. Protein was quantified using DC Protein Assay (Bio-Rad) following the manufacturer's recommendations, with standard curves generated with bovine serum albumin (BSA; Sigma-Aldrich). For H4K20me1 and H4 Western blots, histone extraction was performed with the Epiquik Total Histone Extraction Kit (EpiGentek), following the manufacturer's recommendations. Protein was quantified using Bradford Protein Assay (Bio-Rad).
Ten micrograms or 20 g of total protein lysate was loaded per lane of 4 to 20% bis-tris polyacrylamide mini gels (Invitrogen). SDS-polyacrylamide gel electrophoresis was run at 150 V for 1.5 to 2 hours. Proteins were transferred to nitrocellulose or polyvinylidene difluoride membranes by wet transfer for 90 min at 100 V. Membranes were briefly washed once in diH 2 O, followed by blocking with 5% nonfat dry milk (Bio-Rad) or 5% BSA in tris-buffered saline supplemented with Tween 20 (0.1%) (TBS-T) for 60 min at room temperature. After blocking, membranes were washed briefly with TBS-T and incubated on a plate shaker overnight at 4°C or for 1 hour at room temperature with primary antibodies diluted in TBS-T or 5% BSA/TBS-T. Membranes were washed with TBS-T, followed by incubation with appropriate horseradish peroxidase-conjugated secondary antibodies diluted in TBS-T + 1 to 2% nonfat dry milk for 30 to 60 min at room temperature on a plate shaker. Membranes were washed extensively with TBS-T. Signal was detected using Luminata Crescendo detection system (EMD Millipore) following the manufacturer's recommendations. All antibodies used are listed in table S1.

Immunohistochemistry
PHF8 immunohistochemistry was performed on formalin-fixed, paraffin-embedded slides. Samples were deparaffinized, and heatinduced epitope retrieval was performed using a 1100-W microwave oven and a Nordic Ware pressure cooker filled with enough epitope retrieval solution to cover the slides. The epitope retrieval solution contains Tween, 10 mM tris, 1 mM EDTA, and 0.05% Tween 20; the pH of the solution was adjusted with HCl to pH 9.0. We use VECTASTAIN ABC, Vector Laboratories, following the manufacturer's protocol, and a rabbit polyclonal antibody raised against mouse/human PHF8 at 1:1000 dilution. The slides were counterstained with hematoxylin and permanent-mounted with Permount. Samples were provided by the Biospecimen Core of the NYU Langone Health Interdisciplinary Melanoma Collaborative Group (IMCG). The study protocol was approved by the NYU Institutional Review Committee. All NYU patients signed informed consent. The slides were reviewed and scored by an IMCG pathologist (F.D.) according to the intensity (0, 1+ or 2+) of the staining as well as distribution (percentage of tumor with positive staining; focal: F < 50%; diffuse: D ≥ 50%). The IMCG pathologist was blinded while scoring the samples.

Enzyme-linked immunosorbent assay
To quantitatively determine the amount of TGFB1 produced by melanoma cell lines, the Quantikine Human TGF-1 ELISA kit (R&D Systems) was used according to the kit instructions. A representative experiment in using four technical replicates is shown out of three independent experiments.

In vitro proliferation assay
Transduced cells were seeded at 2 × 10 3 cells per well in 96-well plates, with the aim of fixing one plate per day for up to 5 days after seeding. The next day (day 0) and every 24 hours, cells seeded were fixed in 0.1% glutaraldehyde and stored in PBS at 4°C. At completion of the experiment, cells were stained with 0.5% crystal violet, washed, and left to dry before being dissolved with 15% acetic acid. Optical density was read at 590 nm. For normalization and control purposes, all conditions of an experiment were seeded on the same plate per day.

In vivo metastasis
In vivo mouse experiments were performed in compliance with a referenced protocol (160719-03) approved by the NYU Institutional Animal Care and Use Committee. Four-to six-week-old nonobese diabetic (NOD)/Shi-scid/IL-2R null (NOD.Cg-Prkdc scid Il2rg tm1Wjl /SzJ) female mice were purchased from The Jackson Laboratory and maintained under standard pathogen-free conditions. Experimental sample size was based on our previous experience using this xenograft model system.
451Lu cells transduced with a luciferin-expressing construct and selected with puromycin (2 g/ml) were further transduced with Cas9 lentiviral particles and selected with blasticidin and then sgScr-or sgPHF8#1-carrying lentiviruses. For the xenograft injections, cells were resuspended in growth media at a concentration of 1 × 10 6 cells/150 l, aliquoted into Eppendorf tubes (150 l), and maintained on ice until injection. Immediately before injection, cell aliquots were mixed with 150 l of Matrigel (Becton Dickinson). Cell/Matrigel (1:1) suspensions were injected subcutaneously in the flank. No samples were excluded from the analysis. When tumors were palpable (13 days after xenograft injection), length and width measurements were made with calipers twice weekly until the animals were euthanized. When tumors were palpable, primary flank tumors were measured twice weekly by caliper [length (l) and width (w)] until resected. Tumor volumes were estimated by the formula: (w 2 × l)/2. Forty days after subcutaneous injection, all animals were euthanized. To measure lung bioluminescence ex vivo, mice were injected with d-luciferin (Gold Biotechnologies) substrate into the intraperitoneal cavity at a dose of 150 mg/kg body weight (25 mg/ml of luciferin) 15 min before anesthesia with isoflurane/oxygen, euthanasia, and organ extraction. Lungs were placed on the imaging stage, and a critical 5-min wait between euthanasia and imaging was conserved for all animals, to ensure that luminescence can still be measured and results were comparable between mice. Images were collected by automatic exposure (0.5 s to 2 min) using In vivo imaging system (IVIS) (Xenogen Corp., Alameda, CA). Analysis was performed using Living Image software (Xenogen) by measurement of average radiance (measured in photons/s/cm 2 /steradian) with a region of interest drawn around the lung to be measured. Data were plotted using GraphPad Prism, and significance was determined by unpaired t test.
Macroscopic images of metastasis-bearing organs were taken with a fluorescent dissecting microscope equipped with a color camera (Leica) before fixation. Metastatic lesions were only present in the lungs. Organs (lungs, liver, kidney, ovaries, and brain) were collected, rinsed briefly in Ca 2 + -and Mg 2 + -free 1× PBS, fixed in 10% buffered formalin for 48 hours, and embedded in paraffin following standard conditions. Lung sections were sliced at three different levels, followed by hematoxylin and eosin (H&E) staining, and the meta static foci were counted by a pathologist (F.D.) who was blinded to the denomination of samples.
For each condition of the PHF8 ChIP-seq, 50 g of chromatin was diluted in ChIP buffer, precleared with BSA-blocked Protein A Dynabeads (Invitrogen) for 1 hour at 4°C and then incubated with antibodies conjugated to BSA-blocked Protein A Dynabeads at 4°C overnight on an orbital shaker. The beads were washed seven times with RIPA buffer [50 mM Hepes (pH 7.6), 300 mM LiCl, 1 mM EDTA, 1% Igepal CA-630, and 0.7% sodium deoxycholate]. The beads were additionally washed twice with Tris-EDTA (TE) buffer [10 mM tris (pH 8.0) and 1 mM EDTA] supplemented with 200 mM NaCl. Then, beads were resuspended in 100 l of elution buffer (100 mM sodium bicarbonate and 1% SDS) and incubated for 30 min at 65°C, with orbital shaking (1500 rpm). Eluates were collected, and each ChIP sample and 10% of the input resuspended in elution buffer were incubated for 30 min at 37°C with ribonuclease A (RNase A; 0.5 g/ml) (Sigma-Aldrich). Last, Proteinase K (Roche) was added to each sample at a final concentration of 100 g/ml, and cross-linking was reversed at 65°C overnight. DNA was extracted using a PCR purification kit (Qiagen) and further processed for sequencing or qPCR. ChIP-seq libraries were prepared with KAPA HyperPlus Kits (KAPA) according to the manufacturer's instructions.
For H3K9me1 and H4K20me1 ChIP-qPCR, the protocol used for the ENCODE project was followed with slight modifications. Chromatin cross-linking was performed as described above. For ChIP, cells were lysed with nuclear lysis buffer [ , incubated with RNase A for 1 hour at 37°C, and then incubated with Proteinase K overnight at 65°C for decross-linking. DNA was extracted using a PCR purification kit (Qiagen), and qPCR was performed with the primers indicated in table S2.
ChIP-seq analysis was done using the in-house-developed subpipeline (55). Specifically, sequencing reads of PHF8 and inputs were aligned to reference genome hg19 using Bowtie2 (56) with a default parameter. Model-based analysis of ChIP-Seq (MACS) is used for peak calling with narrow peak calling mode and fold enrichment compared to the inputs is calculated (57). Significant differential peaks for each replicate were called separately with fold enrichment score > 1.5 and q > 0.05 as cutoff. A total of 6118 PHF8 targets were defined as genes with significant differential peaks overlapping with TSS regions (±1 kb) in all three ChIP-seq replicates. The heatmap of PHF8 binding sites was designed using seqMINER (https://ncbi. nlm.nih.gov/pubmed/21177645).

RNA-seq and analysis
RNA was extracted from three biological replicates using a QIAGEN RNeasy minikit. RNA quality was defined on an Agilent 2100 Bioanalyzer, then processed with the Ribo-Zero rRNA Removal Kit (Illumina) to remove ribosomal RNA (rRNA), and further processed into sequencing libraries using the Illumina ScriptSeq Complete Gold kit following the manufacturer's protocol. All libraries were sequenced on Illumina HiSeq2500 (~150 M, 50 bp paired-end) with individual samples spread across multiple sequencing lanes. Indexed sample data were demultiplexed, and individual FASTQ files were generated, followed by quality control assessment with FASTQC. RNA-seq analysis was done using the in-house-developed subpipeline (55). Specifically, sequencing reads of sgRNA knockdown samples and their control samples aligned to reference genome hg19 using STARaligner version 2.4.2 (58) with parameters suggested by TCGA expression mRNA-seq pipeline (https://docs.gdc.cancer.gov/Data/ Bioinformatics_Pipelines/Expression_mRNA_Pipeline/), and the raw read counts were generated. Then, DESeq2 was used to perform differential expression analysis between sgRNA samples and control samples (59). The volcano plot was generated by R. Genes with P < 0.05 and log 2 fold change lower than −0.25 and higher than 0.25 were considered significantly differentially expressed. Fold change and P value of differentially expressed gene sets have been imported into QIAGEN's Ingenuity Pathway Analysis (IPA QIAGEN Redwood City, http://ingenuity.com) for pathway analyses, setting a log 2 fold change lower than −0.3 and higher than 0.3 as cutoff. Area proportional Venn diagrams were generated with BioVenn (https://ncbi. nlm.nih.gov/pubmed/18925949).

Statistical analyses
Statistical analyses were performed with GraphPad Prism (GraphPad Software Inc.). Data are presented as the means ± SD. Significance was determined using unpaired/paired Student's t test, Mann-Whitney test, or log-rank test (Kaplan-Meier curves), where appropriate. The statistical analyses were performed, and P values were indicated in each figure legend. Correlations were analyzed by Spearman correlation in GraphPad Prism. P values are represented as *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, and *****P < 0.00001.